The Path to Consensus on Artificial Intelligence Assurance

Published

March 15, 2022

Author(s)

Laura Freeman, Feras Batarseh, D. Richard Kuhn, M S Raunak, Raghu N. Kacker

Abstract

Widescale adoption of intelligent algorithms requires that Artificial Intelligence (AI) engineers provide assurances that an algorithm will perform as intended. Providing such guarantees involves quantifying capabilities and the associated risks across multiple dimensions including: data quality, algorithm performance, statistical considerations, trustworthiness, security, as well as explainability. In this article we suggest a path forward for the formalization of AI assurance, including its key components.

Citation

Computer (IEEE Computer)

Volume

55

Issue

3

Pub Type

Journals

Download Paper

https://doi.org/10.1109/MC.2021.3129027

Local Download

Keywords

artificial intelligence, data quality, software testing, statistics

Citation

Freeman, L. , Batarseh, F. , Kuhn, D. , Raunak, M. and Kacker, R. (2022), The Path to Consensus on Artificial Intelligence Assurance, Computer (IEEE Computer), [online], https://doi.org/10.1109/MC.2021.3129027, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932907 (Accessed May 3, 2024)